With the proliferation of software-defined radio technology, malicious jamming attacks against wireless communications have become more aggressive and flexible, which could easily create a complex and highly dynamic jamming environment by varying both the jamming parameters and the jamming policies. Such a complex jamming environment makes it challenging for most of deep reinforcement learning (DRL) based anti-jamming schemes in rapidly identifying effective strategies. In this paper, we have developed a dual-tier policy-oriented anti-jamming (DPA) scheme based on DRL to facilitate swift adaptation to the complex jamming environment. Unlike existing works, an upper-tier jamming pattern recognition (JPR) network is introduced to extract underlying jamming policy-related information which serves as a guidance for the lower-tier deep recurrent Q-network on anti-jamming decision-making. The output of the JPR network can enable the sharing of experiences among various jamming patterns originated from the same jamming policy and facilitate more efficient and targeted anti-jamming strategic learning. Extensive experimental results demonstrate that the superiority of our DPA scheme over other DRL-based benchmark schemes in terms of both anti-jamming performance and convergence speed.

The implementation of deep learning-based automatic modulation recognition (AMR) on resource-constrained edge devices calls for efficient networks. Unfortunately, existing AMR networks fail to balance parameter scale, inference speed, and recognition accuracy, which hinders their edge applications. In view of this, we propose an expert-assistant network for efficient AMR with small parameter scale and inference time. To exploit the advantages of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for parameter scale reduction, we build a lightweight AMR network with a CNN-RNN hybrid architecture. Given the speed-and-accuracy dilemma faced by existing CNN-RNN hybrid networks, we introduce an small-scale plug-in, called the assistants, as well as a temporal shuffling scheme to enable fast and accurate AMR with small parameter scales. Besides, techniques like non-recurrent dropout for the gated recurrent unit (GRU) layer, parameter estimator and transformer (PET) and model pruning are applied to further enhance the performance of the networks. Extensive experimental results demonstrate that the proposed Expert-Assistant (E-A) network achieves the best comprehensive performance on lightweight, computational efficiency and recognition accuracy. Our model performs especially well with extremely low parameters, which achieves an average accuracy near 60% and a highest accuracy near 90% with just 3.5K non-zero parameters on RML2016.10a.

This paper studies the beamforming design for jamming assisted multi-user covert communications under the surveillance of an eavesdropper. Different from existing work, the impact of multi-user interference as well as the tradeoff between the jamming and the confidential signal power allocation are taken into consideration. To degrade the eavesdropper's signal detection capability, the power of the jamming signal is often randomized, which introduces uncertainty to the achievable transmission rate. In light of this, we propose to carry out the beamforming design with the concept of quantile transmission rate (QTR) so that the expected covert transmission rate can be achieved with high probability. With QTR, the fairness aware multi-user beamforming design problem is formulated to maximize the minimum QTR among all legitimate users while ensuring the covertness constraints are satisfied. Given the nonconvexity of the constraints, we propose a successive convex approximation (SCA)-based iterative algorithm for effective solution finding. The validity of the obtained covert beamforming scheme is evaluated through various experiments.

An efficient 3D spatial spectrum prediction method is essential for UAV networks operating in highly heterogeneous spectrum environments, enabling UAVs to proactively navigate toward areas with abundant available spectrum and make decisions to access to idle ones in advance. This paper introduces a novel Multi-Scale Temporal model for 3D spatial spectrum prediction (MST-3DSSP) that comprehensively captures complex correlations across 3D spatial, frequency, and multi-scale temporal domains. Specifically, the proposed model incorporates a 3D Spatial-Frequency Fusion (3DS-FF) module to extract and fuse 3D spatial and frequency features, along with a MultiScale Temporal Extraction (MS-TE) module that combines BiLSTM and Transformer blocks to capture both small scale and large scale temporal dependencies. These two modules enable the model to understand the complex correlations across 3D spatial, frequency, and multi-scale temporal domains, thereby allowing for more accurate spectrum predictions. Extensive experiments on real-world spectrum datasets demonstrate that MST-3DSSP significantly outperforms existing spectrum prediction methods, achieving higher prediction accuracy and reduced errors, thus providing a robust solution for improving spectrum efficiency in UAV networks.

Integrated sensing and communication (ISAC) has emerged as a promising technology for future millimeter wave (mmWave) networks. However, the susceptibility of mmWave signals to blockages poses considerable challenges for ISAC as it can result in unreliable links and disrupted sensing. As a result, this paper investigates the blockage-resilient ISAC design that leverages the robustness offered by multi-base station (BS) collaboration. Given the dynamic blockages and the fluctuation in the targets’ radar cross section (RCS), the blockage-resilient multi-BS collaborative ISAC design is cast as a chance constrained integer programming (CCIP) by jointly considering the diverse deadlines of different sensing tasks and the spatial/temporal user-target pairing for dual-functional radar and communication (DFRC) waveform scheduling. To facilitate efficient solution finding, we develop a group concatenating assisted reinforcement learning (GCRL) algorithm, where we linearize the chance constraints via variable grouping and concatenation, enabling the RL agent to understand the problem structure with bipartite graphs so as to develop an efficient branching policy. Extensive experiments demonstrate the resilience of the obtained ISAC scheme to dynamic blockages.

Integrated sensing and communication (ISAC), which integrates sensing capabilities into wireless communication networks, is emerging as a key technology for future millimeter wave (mmWave) communication networks. Given the limited ISAC capability and energy budget of a single base station (BS), this paper studies how to enable energy-efficient sensing and communication via multi-BS collaborative sensing, where each sensing task is served by its most energy-efficient BS as much as possible, with the help of other BSs. Since unregulated multi-BS collaboration may lead to energy wastage and further aggravates the energy consumption in mmWave networks, an energy-efficient collaborative ISAC scheme is proposed, where multi-BS collaborative sensing and dual-functional radar and communication (DFRC) beams are judiciously utilized to reduce the network’s energy consumption. We formulate the design of the energy-efficient collaborative ISAC scheme as a mixed integer nonlinear programming problem by jointly considering task allocation, beam scheduling, and transmit power control. Then, an energy-efficient cooperative beam scheduling (EE-CBS) algorithm is developed for efficient solution finding. Through extensive simulations, the proposed scheme is shown to significantly reduce the network’s energy consumption when compared to the scheme without multi-BS cooperation or the utilization of DFRC waveforms.

Given the time-varying radio environments, legitimate users need to conduct covert communications over multiple time slots with different radio propagation conditions, while a warden performs joint signal detection based on the observations collected during these slots. Unfortunately, existing studies mainly deal with the design of covert communication scheme within a single slot where the radio environment remains unchanged. These scheme might suffer performance degradation when legitimate users make transmission decisions over multiple slots with different propagation conditions and a joint covertness constraint over these slots is imposed. In view of this challenge, we investigate the design of covert communication schemes in time-varying radio environments under a multi-slot covertness constraint. Given the coupling between the transmission decisions in different slots, we formulate the schematic design as a Markov decision process where the multi-slot covertness constraint is characterized with the concept of conditional value at risk to address the non-cumulative growth brought by the unknown eavesdropping channel. Unlike existing works, our scheme allows the legitimate users to adapt their transmission decisions to the current propagation condition and covertness margin. Extensive simulations demonstrate that our scheme can facilitate efficient covert data transmission under a multi-slot covertness constraint.

This article presents a novel noncoherent multisatellite weak signal acquisition scheme by aggregating the observations at multiple low-Earth orbit (LEO) satellites. Existing aggregation schemes rely on exhaustive search over grids on Earth surface to compensate for the differences in the delay and the Doppler frequency shift experienced at different satellites, which have high-computational complexity due to the wide coverage of LEO satellites. Motivated by this observation, we propose to directly work with satellites’ time-frequency (TF) grid and facilitate efficient delay and Doppler compensation by gradually narrowing down the search space over each satellite’s TF grid with multisatellites observations. Our scheme employs geometric search space reduction scheme to reduce the search space for possible Doppler frequency shift and utilizes hierarchical geometric correlation peak matching to eliminate fake correlation peaks based on multisatellite observations. Through extensive performance evaluation, we demonstrate that, in comparison with existing schemes, our proposed multisatellite signal acquisition scheme can achieves significantly better acquisition performance with a much lower computational complexity.

Dynamic spectrum access (DSA) has been regarded as a viable solution to the spectrum shortage problem. To find idle spectrum, partial spectrum sensing could be employed by selecting a suitable sensing window (SW). Since the SW selection determines how many available bands to access, the transmission performance after the access could be used to guide the SW selection. Hence, a sophisticated joint design on spectrum sensing and access is necessary, which, however, is a challenging task when considering the dynamic nature of spectrum environment, and also the mutual impact among different secondary users (SUs). In this paper, we propose a joint partial spectrum sensing and power allocation (PA) scheme to facilitate SUs to make the best decisions on SW and PA to maximize the network throughput with reduced mutual interference. Considering the environmental dynamics and spectrum uncertainty, we develop a viable solution based on hierarchical multi-agent deep reinforcement learning (HMADRL). Our solution enables mutual design with two stages: making each SU learn the best SW and PA strategies autonomously while adapting to the dynamic environment. By using both simulated spectrum data and real spectrum data measured by SAM60-BX, we have demonstrated the effectiveness of our proposed scheme.

Millimeter wave (mmWave) signals are sensitive to blockages in wireless channels. Traditional mmWave transceiver designs intend to harvest both beamsteering and spatial multiplexing gains, but without considering the potential change in the channel state incurred by sudden blockages. In this paper, we propose a blockage-resilient hybrid transceiver design for supporting robust data transmissions in the face of dynamic blockages. Upon exploiting the spatial structure of mmWave channels, we formulate a weighted spectral efficiency maximization problem by utilizing the statistical information concerning the potential future blockages of different path clusters, which uniquely distinguishes this work from existing transceiver optimization problems. On the basis of alternating optimization, we propose a two-stage algorithm to deal with the resultant non-convex problem riddled with highly coupled variables. First, we alternatively optimize the fully digital transmit precoder and receive equalizer by transforming the optimization problem into a quadratic form. Based on the Block Successive Upper-bound Minimization (BSUM) framework, the optimal fully digital precoder and equalizer can be found by exploiting the Karush-Kuhn-Tucker (KKT) conditions and the matrix monotonic method. Then, inspired by the sparse signal recovery philosophy, the hybrid analog/digital transceiver structure is designed for approximating the fully digital solution. Our numerical results show that the proposed design strikes an improved throughput vs. blockage-resilience trade-off compared to existing schemes, which demonstrates its superiority.

The Internet of Remote Things (IoRT) supported by low-Earth orbit (LEO) satellites is becoming indispensable for remote sensing and it will play an important role in the forthcoming sixth-generation (6G) communication network. In exploring its applications in remote mining and smart grid, etc., it is found that the implementation of IoRT faces challenges, including limited energy supplies, high-mobility, and security vulnerabilities. To address these challenges, we propose employing collaborative LEO satellites to enable the implementation of secure and green IoRT. By combining the uplink signals received at collaborative LEO satellites, the signal-to-noise ratio (SNR) can be significantly improved, so that relieving the transmit power requirement of the energy-limited terminal. Aiming at constructing a collaborative LEO satellite-based IoRT network, this article introduces the system design principles regarding to frequency planning, waveform selection, collaboration strategies, and terminal design. In order to obtain optimal collaboration performance, we propose a signal coherent combining scheme to compensate Doppler shift, propagation delay, and phase differences. Furthermore, we propose a modified SUMPLE algorithm to estimate and compensate phase differences among satellites, which is applicable to direct-sequence spread spectrum (DSSS) signal scheme. Simulation results demonstrate that our proposed algorithm outperforms the traditional SUMPLE algorithm in combining gain.

Vehicles’ mobility causes frequent beam misalignments in millimeter wave (mmWave) vehicle-to-infrastructure (V2I) communications. In 5G systems, beam sweeping is done repeatedly to track a vehicle's mobility and maintain high-quality beam selection. Despite extensive studies on mmWave communications, there are still insufficient discussions on when to trigger beam sweeping for beam tracking. Triggering beam sweeping at an inappropriate time can either incur unnecessary overhead or degrade communication performance due to outdated beam selection. Based on this observation, we explore the problem of making beam-sweeping decisions for 5G mmWave vehicular communications. Considering the regularity in vehicle mobility, we propose aContext-aware standard-compatiBle beam update scheme (CarBeam) to help the base station determine when to trigger beam sweeping by exploiting the noisy and quantized beam-specific layer-1 reference signal received power feedback from the vehicle. Unlike prior work,CarBeamonly exploits the procedures and signaling supported in the current 5G beam management framework. Moreover, it can adapt its beam-sweeping decisions to vehicles's mobility for efficient beam tracking. The effectiveness ofCarBeamis evaluated and demonstrated using the vehicle traces from the TAPASCologne project.

The emerging Internet of Things (IoT) paradigm makes our telecommunications networks increasingly congested. Unmanned aerial vehicles (UAVs) have been regarded as a promising solution to offload the overwhelming traffic. Considering the limited spectrums, cognitive radio can be embedded into UAVs to build backhaul links through harvesting idle spectrums. For the cognitive UAV (CUAV) assisted network, how much traffic can be actually offloaded depends on not only the traffic demand but also the spectrum environment. It is necessary to jointly consider both issues and co-design the trajectory and communications for the CUAV to make data collection and data transmission balanced to achieve high offloading efficiency, which, however, is non-trivial because of the heterogeneous and uncertain network environment. In this paper, aiming at maximizing the energy efficiency of the CUAV-assisted traffic offloading, we jointly design the Trajectory, Time allocation for data collection and data transmission, Band selection, and Transmission power control ($\text{T}^{\mathrm{ 3}}\text{B}$) considering the heterogeneous environment on traffic demand, energy replenishment, and spectrum availability. Considering the uncertain environmental information, we develop a model-free deep reinforcement learning (DRL) based solution to make the CUAV achieve the best decision autonomously. Simulation results have shown the effectiveness of the proposed DRL-$\text{T}^{\mathrm{ 3}}\text{B}$strategy.

To deal with the ever increasing wireless traffic, we have recently designed a vehicular cognitive capability harvesting network (V-CCHN) architecture to leverage vehicles as an alternative “transmission medium” (i.e., an opportunistic data carrier), besides the wireless spectrum, to effectively transport data from the location where it is collected to the place where it is consumed or utilized in a smart city environment. In the V-CCHN, cognitive radio technologies are utilized so that a large amount of data can be exchanged between vehicles and roadside infrastructure through short-range high-speed transmissions. Considering the limited contact duration and the uncertain activities of primary users, how to facilitate efficient data exchange between vehicles and roadside infrastructure is very challenging. This problem is further complicated by the fact that the mobility of vehicles might not be accurately predicted. In this paper, we propose a probabilistic data prefetching (PDP) scheme for the V-CCHN to address these challenges. By considering the conditional value at risk, we formulate the PDP schematic design as an optimization problem which allows us to obtain the corresponding PDP scheme. Finally, we have conducted extensive study to evaluate the performance of the obtained PDP scheme under various parameter settings.

Due to their sparsity, 60GHz channels are characterized by a few dominant paths. Knowing the angular information of their dominant paths, we can develop various applications, such as the prediction of link performance and the tracking of 802.11ad devices. Although they are equipped with phased arrays, the angular inference for 802.11ad devices is still challenging due to their limited number of RF chains and limited phase control capabilities. Considering the beam sweeping operation and the high communication bandwidth of 802.11ad devices, we propose variation-based angle estimation (VAE), calledVAE-CIR, by utilizing beam-specific channel impulse responses (CIRs) measured under different beams and the directional gains of the corresponding beams to infer the angular information of dominant paths. Unlike state-of-the-arts,VAE-CIRexploits the variations between different beam-specific CIRs for angular inference and provides a performance guarantee in the high signal-to-noise-ratio regime. To evaluateVAE-CIR, we generate the beam-specific CIRs by simulating the beam sweeping of 802.11ad devices with the beam patterns measured on off-the-shelf 802.11ad devices. The 60GHz channel is generated via a ray-tracing-based simulator and the CIRs are extracted via channel estimation based on Golay sequences. Through extensive experiments,VAE-CIRis shown to achieve more accurate angle estimation than existing schemes.

Ubiquitous powerful personal computing facilities, such as desktop computers and parked autonomous cars, can function as micro edge computing servers by leveraging their spare resources. However, to harvest their resources for service provisioning, two significant challenges will arise: how to incentivize the server owners to contribute their computing resources, and how to guarantee the end-to-end (E2E) Quality-of-Service (QoS) for service buyers? In this paper, we address these two problems in a holistic way by advocating COMSA. Unlike the existing double auction schemes for edge computing which mostly focus on computing resource trading, COMSA addresses the joint problem of double auction mechanism design and network resource allocation by explicitly taking spectrum allocation and data routing into account, thereby providing E2E QoS guarantees for edge computing services. To handle the design complexity, COMSA employs a two-step procedure to decouple network optimization and mechanism design, which hence can be applied to general network optimization problems for edge computing. COMSA holds some critical economic properties, i.e., truthfulness, budget balance, and individual rationality. Our extensive simulation studies demonstrate the effectiveness of COMSA.

Battery-powered wireless IoT devices are now widely seen in many critical applications. Given the limited battery capacity and inaccessibility to external power recharge, optimizing energy efficiency (EE) plays a vital role in prolonging the lifetime of these IoT devices. However, a sheer amount of existing works only focus on the EE design at the infrastructure level such as base stations (BSs) but with little attention to the EE design at the device level. In this paper, we propose a novel idea that aims to shift energy consumption to a grid-powered cognitive radio mesh network thus preserving energy of battery-powered devices. Under this line of thinking, we cast the design into a cross-layer optimization problem with an objective to maximize devices’ energy efficiency. To solve this problem, we propose a parametric transformation technique to convert the original problem into a more tractable one. A baseline scheme is used to demonstrate the advantage of our design. We also carry out extensive simulations to exhibit the optimality of our proposed algorithms and the network performance under various settings.

In this paper, we investigate the energy minimization problem for a cognitive capacity harvesting network (CCHN), where secondary users (SUs) without cognitive radio (CR) capability communicate with CR routers via device-to-device (D2D) transmissions, and CR routers connect with base stations (BSs) via CR links. Different from traditional D2D networks that D2D transmissions share the resource of cellular transmissions in the same cell, we consider the scenario that D2D transmissions share the uplink cellular frequency bands (CFBs) of neighbor cells. To ensure that the transmissions from SUs do not affect the transmissions for the cellular users (CUs) in the neighbor cells, an inter-cell handshake process is proposed. We formulate the energy minimization problem for SUs as a mixed integer non-linear programming (MINLP). To solve this problem, we decompose it into two nested subproblems: a transmit power optimization subproblem and a CR router and uplink CFB selection subproblem. For the first subproblem, it is proved to be convex, and thus can be efficiently solved. For the second subproblem, we propose a two-level nested game theoretic approach to finding its solution. Simulation results show that the proposed algorithms can significantly improve the performance. With the help of CR routers/the neighbor resource sharing, the energy consumption for SUs can be saved around 30%-37% on average.

With the rapid growth on data traffic, spectrum shortage becomes increasingly serious, leading to the paradigm shift in spectrum usage from an exclusive mode to a sharing mode. However, how to utilize shared spectrums effectively for service provisioning is not straightforward due to its uncertain availability, known as spectrum uncertainty. In this paper, we propose a new metric to evaluate the achievable rate of a link on a share band under a confidence level, called probabilistic link capacity, which offers us an effective way to guarantee the quality of service statistically when using the shared spectrum for service delivery. Different from most existing works where the distributional information is explicitly given based on certain structural assumption, we develop a data-driven distributionally robust approach by using the first and second order statistical information. To achieve the result, we formulate it into a tractable semidefinite programming problem based on the worst-case of conditional-value-at-risk. Finally, as a use case, we design a service-based spectrum-aware transmission scheme, so that different kinds of spectrums (licensed and shared) can be efficiently utilized to satisfy the diverse service requirements.

In this paper, we introduce a network entity called point of connection (PoC), which is equipped with customized powerful communication, computing, and storage (CCS) capabilities, and design a data transportation network (DART) of interconnected PoCs to facilitate the provision of Internet of Things (IoT) services. By exploiting the powerful CCS capabilities of PoCs, DART brings both communication and computing services much closer to end devices so that resource-constrained IoT devices could have access to the desired communication and computing services. To achieve the design goals of DART, we further study the spectrum-aware placement of edge computing services. We formulate the service placement as a stochastic mixed-integer optimization problem and propose an enhanced coarse-grained fixing procedure to facilitate efficient solution finding. Through extensive simulations, we demonstrate the effectiveness of the resulting spectrum-aware service placement strategies and the proposed solution approach.

The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles' positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. The theoretical and simulation results are provided to validate the feasibility of the proposed schemes.

WiFi's energy efficiency is a critical issue for battery-powered mobile devices. Since wireless channel has inherent broadcast nature, a non-negligible amount of a device's energy is spent on overhearing useless packets that are not addressed to itself. To resolve packet overhearing problem, most existing schemes are limited to decode data packet or exchange control packet to obtain extra information. In this paper, we propose PhyCast (Physical layer broadCast), a novel communication scheme to embed lightweight information into the front part of data transmission at the physical layer. With PhyCast, the transmitter's neighboring nodes extract information by symbol level energy detection, which does not require receiving and decoding the whole data packet. Therefore, unintended receivers can quickly drop useless packet and switch to a low-power state. The design of PhyCast does not affect the correct decoding of a data packet or sacrifice the normal data throughput. In addition, the communication scheme PhyCast is transparent to the existing WiFi devices, so PhyCast is backward compatible with the 802.11 standard. Our simulation results show that PhyCast achieves significant energy efficiency improvement under various network settings. When a WiFi network includes 15 nodes, PhyCast saves 36.85% energy compared with the 802.11 standard.

With the growth of smartphone sales and app usage, fingerprinting and identification of smartphone apps have become a considerable threat to user security and privacy. Traffic analysis is one of the most common methods for identifying apps. Traditional countermeasures towards traffic analysis includes traffic morphing and multipath routing. The basic idea of multipath routing is to increase the difficulty for adversary to eavesdrop all traffic by splitting traffic into several subflows and transmitting them through different routes. Previous works in multipath routing mainly focus on Wireless Sensor Networks (WSNs) or Mobile Ad Hoc Networks (MANETs). In this paper, we propose a multipath routing scheme for smartphones with edge network assistance to mitigate traffic analysis attack. We consider an adversary with limited capability, that is, he can only intercept the traffic of one node following certain attack probability, and try to minimize the traffic an adversary can intercept. We formulate our design as a flow routing optimization problem. Then a heuristic algorithm is proposed to solve the problem. Finally, we present the simulation results for our scheme and justify that our scheme can effectively protect smartphones from traffic analysis attack.

Given the fact that roughly 800 000 children are reported missing in the United States every year, how to assist parents to track their children becomes an important problem. Even though many children tracking systems have been proposed, the high cost and energy limitation of locators are the stumbling blocks which limit the application of those systems. To address this challenge, we design a children tracking system based on RFID technology, where children carry RFID tags and the system is responsible for locating the children by aggregating the readings from the deployed readers. Noting the importance of localized processing for efficient children tracking, we further study how the locally available computing resource, such as the mobile devices carried by the park employees and visitors, can be utilized for service provisioning. Since mobile devices have limited energy, we study an energy efficiency optimization problem by jointly considering the resource allocation and user association. The formulated problem is solved by a dynamic updating matching approach. Through extensive simulations, we have demonstrated the effectiveness of our proposed solution.

Communication technologies supply the blood for smart city applications. In view of the ever-increasing wireless traffic generated in smart cities and our already congested radio access networks (RANs), we have recently designed a data transportation network, the vehicular cognitive capability harvesting network (V-CCHN), which exploits the harvested spectrum opportunity and the mobility opportunity offered by the massive number of vehicles traveling in the city to not only offload delay-tolerant data from congested RANs but also support delay-tolerant data transportation for various smart-city applications. To make data transportation efficient, in this paper, we develop a spectrum-aware (SA) data transportation scheme based on Markov decision processes. Through extensive simulations, we demonstrate that, with the developed data transportation scheme, the V-CCHN is effective in offering data transportation services despite its dependence on dynamic resources, such as vehicles and harvested spectrum resources. The simulation results also demonstrate the superiority of the SA scheme over existing schemes. We expect the V-CCHN to well complement existing telecommunication networks in handling the exponentially increasing wireless data traffic.

In cognitive radio networks (CRNs), spectrum database has been well recognized as an effective means to dynamically sharing licensed spectrum among primary users (PUs) and secondary users (SUs). In spectrum database, the protected incumbents (a.k.a. PUs) and the CRs (a.k.a. SUs) are required to register in database their operational specifications such as transmitting power, antenna height, time of operation and etc. so as to provide an up-to-date radio map for public queries and avoid possible interference. However, it poses potentially serious privacy problems especially when governmental and military systems participate in spectrum sharing through spectrum database. Most recent research works in database-driven CRNs, however, only focused on protecting user's location privacy but merely studied preserving PUs' operational specifications. In this paper, we propose a secure and privacy-preserving scheme using hidden policy-assisted attribute-based encryption technique to protect sensitive PUs' operational privacy without affecting database's accessibility and spectrum utilization efficiency. The security and performance analysis demonstrates that our scheme is secure and computationally efficient. Additionally, our policy-assisted scheme is practical and promising because of its consistency with FCC/NTIA's rule in spectrum regulation in database-driven CRNs.

This paper conducts outage analysis for cooperative hybrid automatic repeat request with incremental redundancy (C-HARQ-IR). A general time-correlated Nakagami fading channel including fast fading and Rayleigh fading as special cases is considered. An efficient truncated inverse moment matching method is developed to derive the outage probability in closed- form. The accuracy of the analytical results is verified by Monte Carlo simulations and the results reveal that C-HARQ-IR protocol can benefit from high fading order and low channel time correlation.

Auction-based secondary spectrum market provides a platform for spectrum holders to share their under-utilized licensed bands with secondary users (SUs) for economic benefits. However, it is challenging for SUs to directly participate due to their limited battery power and capability in computation and communications. To shift complexity away from users, in this paper, we propose a novel multi-round service-oriented combinatorial spectrum auction with two-tier framework support. In Tier I, we introduce several secondary service providers (SSPs) to provide end-users with services by using purchased licensed bands even if the end-users do not have cognitive radio capability. When an SU submits its service request with certain bidding allowance to its SSP, the SSP will help find out which bands within its area are available and bid for the desired ones from the market in Tier II. Specifically, we formulate the bidding process at the SSP as an optimization problem by considering interference management, spectrum uncertainty, flow routing, and budget allowance. In Tier II, considering two possible manners of the seller, we propose two social-welfare-maximizing auction mechanisms accordingly, including the winner determination based on weighted conflict graph and the Vickrey-Clarke-Groves-styled price charging mechanism. Extensive simulations have been conducted and the results have demonstrated the higher revenue of the proposed scheme compared with the traditional commodity-oriented single-round truthful schemes.

In cognitive radio networks (CRNs), primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, while SUs will in return obtain more spectrum access opportunities, leading to cooperative CRNs (CCRNs). Prior research works in CCRNs mainly focus on providing ubiquitous access and high throughput for users, but have rarely taken energy efficiency into consideration. Besides, most existing works assume that the SUs are passively selected by PUs regardless of SUs' willingness to help, which is obviously not practical. To address energy issue, this paper proposes an energy-efficient cooperative strategy by leveraging temporal and spatial diversity of the primary network. Specifically, SUs with delay-tolerant packets can proactively make the cooperative decisions by jointly considering primary channel availability, channel state information, PUs' traffic load, and their own transmission requirements. We formulate this decision-making problem based on the optimal stopping theory to maximize SUs' energy efficiency. We solve this problem using a dynamic programming approach and derive the optimal cooperative policy. Extensive simulations are then conducted to evaluate the performance of our proposed strategy. The results show significant improvements of SUs' energy efficiency compared with existing cooperative schemes, which demonstrate the benefits of our proposed cooperative strategy in conserving energy for SUs.

When equipped with multiple antennas, secondary users in cognitive radio networks are able to communicate even when neighboring primary users are active by transmitting in the null space of the communication channel occupied by primary users. In this case, the throughput of a secondary link is limited by the transmission power and the dimension of the null space, i.e., the number of active primary users nearby. Since the number of active primary users is time-varying, the required transmission power to support certain data rate changes from time to time. Thus, secondary users could adapt their transmission to the variation of the primary traffic to improve energy efficiency. In view of that, we develop an energy-efficient traffic scheduling scheme for secondary users equipped with multiple antennas. By formulating the traffic scheduling problem as a Markov decision problem, an energy-efficient transmission scheme is derived from linear programming. The analytical results are verified by simulations and the impacts of various parameters are discussed. The superiority of the derived scheme is also shown by comparing with a randomized scheme.

In cognitive radio networks, primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, and SUs will in turn obtain more spectrum access opportunities. While most existing works assume that SUs are passively selected by PUs regardless of SUs' willingness, in this paper, we propose a cooperative strategy for SUs to actively decide whether to cooperate or not. Basically, due to PUs' time-varying traffic demands, it is essential for SUs to firstly observe the channels and then select a specific PU to cooperate with in order to save the energy. In our paper, this decision related problem is formulated based on optimal stopping theory where SUs observe PUs in time sequence and then make decisions whether to stop observation and cooperate right away or wait till next time slot to repeat the same process. We address this problem by using backward induction and derive the energy-efficient strategy for SUs. To validate the feasibility of our proposed scheme, extensive simulations are conducted to show the impact of PUs' traffic demands on SUs' decisions. The results also reveal that the proposed optimal rule outperforms the greedy selection strategy and is thus more energy- efficient to be applied to the cooperative cognitive radio networks.

Advancements in big data analysis offer cost-effective opportunities to improve decision-making in numerous areas such as health care, economic productivity, crime, and resource management. Nowadays, data holders are tending to sharing their data for better outcomes from their aggregated data. However, the current tools and technologies developed to manage big data are often not designed to incorporate adequate security or privacy measures during data sharing. In this paper, we consider a scenario where multiple data holders intend to find predictive models from their joint data without revealing their own data to each other. Data locality property is used as an alternative to multi-party computation (SMC) techniques. Specifically, we distribute the centralized learning task to each data holder as local learning tasks in a way that local learning is only related to local data. Along with that, we propose an efficient and secure protocol to reassemble local results to get the final result. Correctness of our scheme is proved theoretically and numerically. Security analysis is conducted from the aspect of information theory.

In this paper, performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) over Rayleigh fading channels is investigated. Different from prior analysis, time correlation in the channels is considered. Under time-correlated fading channels, the mutual information in multiple HARQ transmissions is correlated, making the analysis challenging. By using polynomial fitting technique, probability distribution function of the accumulated mutual information is derived. Three meaningful performance metrics including outage probability, average number of transmissions, and long term average throughput (LTAT) are then derived in closed-forms. Moreover, diversity order of HARQ-IR is also investigated. It is proved that full diversity can be achieved by HARQ-IR, i.e., the diversity order is equal to the number of transmissions, even under time-correlated fading channels. These analytical results are verified by simulations and enable the evaluation of the impact of various system parameters on the performance. Particularly, the results unveil the negative impact of time correlation on the outage and throughput performance. The results also show that although more transmissions would improve the outage performance, they may not be beneficial to the LTAT when time correlation is high. Optimal rate design to maximize the LTAT is finally discussed and significant LTAT improvement is demonstrated.

In this paper, the performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) in a mobile ad hoc network (MANET) is analyzed. Based on the theory of stochastic geometry, both interference and the randomness in nodes' locations are incorporated into our analysis. The outage probability after the kth retransmission is analyzed and the outage performance of HARQ-IR is compared with that of Type-I HARQ and HARQ with chase combining (HARQ-CC). Our analysis reveals that the performance gains of HARQ-IR over Type-I HARQ and HARQ-CC increase with the number of retransmissions while they decrease with the path loss exponent. The network throughput in terms of transmission capacity is also analyzed. The results indicate that a certain small number of retransmissions is sufficient to achieve the maximal transmission capacity. Meanwhile, the optimal intensity of source nodes to maximize the transmission capacity is shown to be within a specific interval.

In this paper, an opportunistic cooperative HARQ system is analyzed. Different from prior analyses, time correlated fading channels are considered. Based on moment generation function and Laplace transform, the outage probability and throughput in terms of long-term average transmission rate (LATR) of this opportunistic cooperative HARQ system are derived in closed-forms. The accuracy of the analytical results is verified by computer simulations. From the analytical results, the impacts of the time correlation and other system parameters on the performance are investigated and the optimal packet rate selection to maximize the LATR is discussed.

In this paper, the outage performance of an opportunistic amplify-and-forward cooperative downlink cellular system is analyzed. Different from prior works, the randomness of the network topology is taken into account by modeling the user nodes as a homogeneous Poisson point process. Based on this model, outage probability is derived and the impacts of several system parameters are investigated. Under certain conditions, the closed form expression of outage probability is derived. It is found from our results that the diversity order of this opportunistic cooperative system at high signal-to-noise-ratio (SNR) is one. Moreover, optimal power allocation can be found from our results to minimize the outage probability.

In this paper, outage performance of hybrid automatic repeat request (HARQ) technique in interference dominant mobile ad hoc networks (MANETs) is analyzed. Unlike prior analysis, interference and spatial randomness of the nodes (i.e. the randomness in the number of nodes and nodes' locations) are considered. Based on the theory of point processes, the outage probabilities of two popular HARQ techniques, that are type-I HARQ and HARQ with chase combining (HARQ-CC), are derived in closed forms. The outage performance gain of HARQ-CC over type-I HARQ is also discussed and is found to follow the scaling law of Θ (k2(k+1)/α) where α is the path loss exponent and k is the number of retransmissions. It is also demonstrated that both type-I HARQ and HARQ-CC can significantly improve the communication performance even in interference dominant MANETs. Furthermore, it is revealed that in most cases HARQ-CC is superior over type-I HARQ, however, for a dense network type-I HARQ can provide comparable performance with lower complexity than HARQ-CC and is thus more preferable.

In this paper, the uplink coverage probability for heterogeneous cellular systems with range expansion is analyzed and derived in closed-form. Unlike most of the previous analyses of heterogeneous systems, the randomness of not only the number of mobile users but also their locations is taken into account in the analysis based on the theory of stochastic geometry. With the derived analytical results, the impacts of various system parameters on the uplink performance are investigated in detail. The correctness of the analytical results is also verified by simulations. These analytical results can thus serve as a guidance for the design of the heterogeneous systems.

In this paper, the performance of hybrid automatic repeat request (HARQ) technique in an ad hoc network is analyzed. Unlike most prior works on the analysis of HARQ, both time-correlated interference and feedback errors are taken into account in the analysis. Based on the theory of point processes, outage probability after the nth retransmission, delay limited throughput and mean transmission time are derived in closed forms. The analytical results are verified by simulations and the impacts of various parameters on the network performance are investigated in detail. It is found that the outage probability obeys the inverse-2/α power law over the number of retransmissions, where α is the path loss exponent. Furthermore, it is demonstrated that the feedback error significantly degrades the delay limited throughput when the ad hoc network becomes dense, while the increment of mean transmission time due to the feedback error is a concave function of the intensity of the network.